https://doi.org/10.1140/epjqt/s40507-024-00255-9
Research
A meta-trained generator for quantum architecture search
1
School of Electronic and Information Engineering, Foshan University, 528000, Foshan, China
2
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
3
College of Mathematics and Informatics, South China Agricultural University, 510642, Guangzhou, China
4
College of Computer and Information Engineering, Henan Normal University, 453007, Xinxiang, China
5
Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area, 518045, Shenzhen, China
6
Institute of Quantum Computing and Software, School of Computer Science and Engineering, Sun Yat-sen University, 510006, Guangzhou, China
Received:
24
December
2023
Accepted:
1
July
2024
Published online:
9
July
2024
Variational Quantum Algorithms (VQAs) have made great success in the Noisy Intermediate-Scale Quantum (NISQ) era due to their relative resilience to noise and high flexibility relative to quantum resources. Quantum Architecture Search (QAS) aims to enhance the performance of VQAs by refining the structure of the adopted Parameterized Quantum Circuit (PQC). QAS is garnering increased attention owing to its automation, reduced reliance on expert experience, and its ability to achieve better performance while requiring fewer quantum gates than manually designed circuits. However, existing QAS algorithms optimize the structure from scratch for each VQA without using any prior experience, rendering the process inefficient and time-consuming. Moreover, determining the number of quantum gates, a crucial hyper-parameter in these algorithms is a challenging and time-consuming task. To mitigate these challenges, we accelerate the QAS algorithm via a meta-trained generator. The proposed algorithm directly generates high-performance circuits for a new VQA by utilizing a meta-trained Variational AutoEncoder (VAE). The number of quantum gates required in the designed circuit is automatically determined based on meta-knowledge learned from a variety of training tasks. Furthermore, we have developed a meta-predictor to filter out circuits with suboptimal performance, thereby accelerating the algorithm. Simulation results on variational quantum compiling and Quantum Approximation Optimization Algorithm (QAOA) demonstrate the superior performance of our method over a state-of-the-art algorithm, namely Differentiable Quantum Architecture Search (DQAS).
Key words: Quantum Machine Learning / Variational Quantum Algorithm / Parameterized Quantum Circuits / Variational Quantum Compiling
Zhimin He and Chuangtao Chen contributed equally to this work.
© The Author(s) 2024
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